Urban Total Factor Productivity: Does Urban Spatial Structure Matter in China?

Yu, Haidong; Liu, Yong; Jiancheng, Zhao; Li, Gen

doi:10.3390/su12010214

Cited by 8 publications

(4 citation statements)

References 28 publications

(29 reference statements)

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“…The annual growth rate of TC in the nine Pan-PRD provinces is 6.0%, and the annual growth rate of TEC is 0.6% (only Hainan and Guangxi provinces have a negative annual growth rate of TEC), indicating that TC is the main driver of TFP growth in the nine provinces. This is similar to the findings of Yu et al [28] Guo [29], and Li et al [30]. Among them, the growth rate of TC in Hainan and, Guangxi provinces far exceeds the growth rate of TEC especially significantly, indicating that the TEC of the two provinces, especially the scale efficiency, needs to be improved urgently.…”

Section: Heterogeneity Analysis Of Tfp Growth In Nine Pan-prd Provinc...supporting

confidence: 90%

Growth Trends and Heterogeneity of Total Factor Productivity in Nine Pan-PRD Provinces in China

Shi

Zhu

2022

Sustainability

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As a national regional development strategy and a vital region of the Belt and Road Initiative, the sustainable development of the Pan-Pearl River Delta (Pan-PRD) region is of great importance. The national development plan emphasizes improving total factor productivity (TFP) and promoting high-quality economic development. This paper uses the DEA-Malmquist index model to measure the TFP of nine provinces in the Pan-PRD region based on inter-provincial panel data from 2003 to 2020. Furthermore, it analyzes its growth trend and heterogeneity characteristics in the inter-provincial spatial, industrial, and city dimensions. The results show that in the time dimension, TFP shows a W-shaped fluctuation trend, technical efficiency grows slowly, and technical progress is the pillar of TFP improvement. The spatial dimension shows a high distribution in the center and low distribution in the south. On the industry dimension, the TFP is in descending order as follows: tertiary industry—secondary industry—primary industry. The spatial distribution is heterogeneous, exacerbating the uneven economic development within the region, and the regional industrial structure needs urgent optimization. The spatial development of city TFP is uneven, and the number of cities with a TFP below 1 is increasing. Finally, we suggest policies to accelerate regional collaborative innovation, cultivate advantageous industrial clusters, create an advantageous industrial ecosystem, and achieve sustainable development in the Pan-PRD region.

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Section: Heterogeneity Analysis Of Tfp Growth In Nine Pan-prd Provinc...supporting

confidence: 90%

Growth Trends and Heterogeneity of Total Factor Productivity in Nine Pan-PRD Provinces in China

Shi

Zhu

2022

Sustainability

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“…In the process of the rapid urbanization in China, the spatial structure of cities has mostly shown a compact pattern of outward expansion [45]. In contrast, the centers of older urban areas have a longer history of development and an obvious accumulation effect, gathering more high-quality medical-service facilities and tertiary-industry service facilities, such as for education and commerce, in the cities [46].…”

Section: The Spatial Structure Of China's Large Cities Affects the La...mentioning

confidence: 99%

COVID-19: Evaluation of Fever Clinic and Fever Sentinel Configuration—A Case Study of Harbin, China

Feng

et al. 2022

Sustainability

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The COVID-19 pandemic has placed the inequalities in health services in countries around the world under severe pressure. As crucial pillars in the prevention and control of COVID-19, fever clinics and fever sentinels are important sites for the screening, diagnosis, and isolation of patients. This study comprehensively evaluated the spatial-layout characteristics, configuration quantity, and service capacity of 42 fever clinics and 418 fever sentinels in Harbin from the perspective of supply by using GIS spatial-analysis methods such as kernel density analysis. From the perspective of demand, we evaluated the accessibility of fever clinics with the modified two-step floating catchment area (2SFCA) method; the OD cost matrix method and Voronoi diagram method were used to evaluate the accessibility and service pressure of fever sentinels. This study found that a monocentric clustering characterizes the spatial layout of fever clinics, and the design of fever clinics in new urban areas and marginal rural areas is relatively lacking. The spatial layout of fever sentinels includes blank areas, and the service pressure in the central city area is relatively high. Combined with the assessment results, the study discussed optimization strategies and implementation paths for improving the public health and epidemic prevention system for COVID-19 in terms of four aspects: the transformation of governance practice, the spatial-planning response, the digital infrastructure response, and guarantees of policies and regulations.

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“…The panel data model can be divided into the fixed effect model and random effect model. Taking the individual dimension as an example, when α i and X it are correlated (i.e., corr(α i , X it ) 0), the model is a fixed effect model; otherwise, it is a random effect model [39]. In other words, given i, if α i is a certain value, it is a fixed effect model (different individuals have different characteristics, and each individual has a special nominal value).…”

Section: Panel Data Modelmentioning

confidence: 99%

The Impact of Environmental Conditions on Urban Eco-Sustainable Total Factor Productivity: A Case Study of 21 Cities in Guangdong Province, China

Jiancheng

2020

IJERPH

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Environmental protection has attracted much attention. This study first describes the status of the ecological environment and then uses data envelopment analysis and the system the system generalized method of moments (GMM) model to study the relationship between the environmental status and ecological sustainable total factor productivity (ESTFP) in 21 prefecture-level cities of Guangdong Province. The main conclusions of this study are as follows. (1) The ecological index (EI), which reflects the ecological environment, shows a general trend of first decreasing and then rising. The average EI value decreased from 80.95 in 2008 to 68.71 in 2011 and then gradually increased to 74.76 in 2017. (2) The ecological sustainable total factor productivity (ESTFP = 0.960), including the two additional dimensions of the urban resource consumption index (URCI) and urban pollution discharge index (UPDI), is better than the traditional total factor productivity (TFP = 0.954). (3) The EI has a highly significant positive promoting effect on ESTFP at a significance level of 1%. The methods and results from this research provide an important scientific reference for the research on urban production efficiency and sustainable urban development in China.

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Urban Total Factor Productivity: Does Urban Spatial Structure Matter in China?

Cited by 8 publications

References 28 publications

Growth Trends and Heterogeneity of Total Factor Productivity in Nine Pan-PRD Provinces in China

Growth Trends and Heterogeneity of Total Factor Productivity in Nine Pan-PRD Provinces in China

COVID-19: Evaluation of Fever Clinic and Fever Sentinel Configuration—A Case Study of Harbin, China

The Impact of Environmental Conditions on Urban Eco-Sustainable Total Factor Productivity: A Case Study of 21 Cities in Guangdong Province, China

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